Categories: AI/ML News

Robots learn human-like movement adjustments to prevent object slipping

To effectively tackle a variety of real-world tasks, robots should be able to reliably grasp objects of different shapes, textures and sizes, without dropping them in undesired locations. Conventional approaches to enhancing the ability of robots to grasp objects work by tightening the grip of a robotic hand to prevent objects from slipping.

A robot learns to handle bulky objects like humans do after just one lesson

For all their technological brilliance, from navigating distant planets to performing complex surgery, robots still struggle with a few basic human tasks. One of the most significant challenges is dexterity, which refers to the ability to grasp, hold and manipulate objects. Until now, that is. Scientists from the Toyota Research…

September 4, 2025

In "AI/ML News"

Aquatic robot’s self-learning optimization enhances underwater object manipulation skills

In recent years, roboticists have introduced robotic systems that can complete missions in various environments, ranging from the ground to underground, aboveground and underwater settings. While several of these robots can grasp and move objects on the ground, the handling of objects by robotic systems underwater has so far proved…

November 7, 2024

In "AI/ML News"

Reinforcement learning allows underwater robots to locate and track objects underwater

A team has shown that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater robots to locate and carefully track marine objects and animals.

July 31, 2023

In "FAANG"

AI Generated Robotic Content